A while back, I started having problems with the output of Venus, a planet-like aggregator I use to read a bunch of things. The symptoms were broken characters for things like apostrophes, quotes and so on – which rendered the output nearly unusable. I dug into it, but couldn’t resolve the problem…so I resorted to a bletcherous hack (cron job to copy the file to my laptop, and view it with file:///...) and blamed Python 2.

Today I came across the same problem but manifested in another set of files. This time I managed to find the answer:

AddCharset UTF-8           .htm .html .js .css

To be clear, I already:

• had made sure that the headers for the file included Content-Type: text/html;charset=utf-8
• had made sure the html file had <meta charset="utf=8">

Weirdly enough, changing that meta tag to:

<meta http-equiv="Content-Type" content="text/html; charset=utf-8" >

worked…the apostrophes and such were displayed correctly. But they never showed up in the output when I ran a curl on the URL. Does Apache filter this stuff on the fly?

Anyhow…that’s enough encoding debugging for one day. Or possibly a year.

Here’s a quick list, for my own reference, of what I got up to in January. It’s heartening to see everything laid out, and realize that I’ve actually managed to get a fair bit done!

Hardware hacking

• My father-in-law and I worked on getting the precipitation meter going for our weather station. It took a while, but we finally got it working. 🎉

• Some one-wire temperature sensors came in, and I was able to whip up a quick demo to make sure they worked.

• Talked to my father-in-law about building a Lehmann seismograph. Early days, but I think he’s in.

Polaris

• Worked on my upcoming talk on Polaris for PyCascades 2021. (Final version recorded just yesterday.)

• Got a hugo site whipped together for polarisml.space, which now has links to our demo, code, chat and documentation.

• Cut my first release for Polaris after getting our page on PyPi sorted out.

Machine learning

• Some progress, though slow, on going through the FastAI book.

• Tripped over Roboflow, which generates synthetic data for ML; very interesting, and I may give this a try for the dishwasher loading critic.

• Some initial experiments with detecto, a simple wrapper for PyTorch object detection.

Radio

• Not a whole lot of trips out, but some…and managing to reach D4Z Cape Verde on 10W. 9,155 km!

• Totalled up my contacts toward SKCC Centurion…42/100. Normally I’m not big on this sort of thing, but it’s a number to reach for, and that’s no bad thing right now.

Last year, my father-in-law got a trail cam at my suggestion – mainly to get pictures of the rats that were eating his compost. It worked:

I borrowed it a while back, and finally set it up today under our bird feeder to see what we could get. Not a bad haul! We got:

• Spotted towhees:

• Dark-eyed juncos:

• Black and grey squirrels:

• A chickadee:

Not bad!

Out for radio as well: 12 QSOs from the North American QSO contest, including D4Z from Cape Verde – about 9150km on 10W. Nice!

My project: critiquing your dishwasher loading technique using machine learning. A work in progress. You can find the repo here.

I’ve been interested in machine learning for a while now. Like a lot of things, my approach has been a bit scattered. I’m slowly learning how to get better at that, but I still tend to veer around.

A couple of months ago, I decided to take the Fast.ai course again. I had done a couple of lessons a year ago, but had not followed it up. This time around, I saw that they not only had a new version of the course, but a book as well. I ordered the book (and another book as well), and got started on the Jupyter notebooks that the book is based on.

Let’s see where this goes!